专题：中国财富管理50人论坛2025年会 杨龙如介绍，工商银行自2023年以来，在传统人工智能应用基础上，围绕算力、算法、知识、应用范式 与模型安全等核心要素，构建了"工银智涌"大模型技术体系，并以此作为全行智能体创新的企业级数智 基础。目前，该体系已在金融市场、市场营销、客户服务、风险管控等20多个领域落地超过400个应用 场景。 他随后重点剖析了当前大模型在金融行业深度应用面临的四大挑战： 一是基模（基础模型）能力发展仍无法满足行业实际需要。 杨龙如表示，基模在通用任务上表现良 好，但在面对高度复杂的特定金融场景时，其专业能力往往不足。例如，直接使用基模预测小微企业违 约风险，其结果的拟合程度通常不如专业模型。因此，对基模进行领域后训练或采用模型组合应用，仍 是需要投入大量精力的重点工作。 二是高质量金融数据集依然稀缺。 他指出，尽管银行数据丰富，但能为大模型训练所用的行业高质量 数据集仍然短缺。银行内部数据分散、口径不一，专家经验与决策逻辑等隐性知识未能系统化沉淀，传 统的专业技能知识转化为大模型可用数据的工程化路径尚不清晰。以风险管理领域为例，支撑深度思维 链推理的训练数据仍显不足。 三是业务模式变革 ...

Investment Rating - The report suggests a positive investment outlook for the AI and financial services sector, highlighting the potential for significant advancements and cost reductions due to the release of DeepSeek R1 in 2025, which is expected to be a turning point for localized AI deployment in financial institutions [7]. Core Insights - AI applications are rapidly penetrating core business areas and back-office functions of various financial institutions, enhancing both internal efficiency and external value [1][7]. - The report identifies that most financial institutions are currently in the exploration and accumulation phase of AI application, with deep application being an inevitable trend [14]. - AI is expected to transform financial business processes and organizational structures, ushering in a new era of digital intelligence in finance [7]. Summary by Sections Investment Recommendations - The report recommends focusing on several sectors within the financial industry, including: 1. Financial information services with key stocks like Tonghuashun, Jiufang Zhitu Holdings, and Guiding Compass [8]. 2. Third-party payment services, recommending stocks such as Newland and Newguodu, with related stocks like Lakala [9]. 3. Banking IT, with recommended stocks including Yuxin Technology, Jingbeifang, and Guodian Yuntong [9]. 4. Securities IT, recommending stocks like Hengsheng Electronics and Jinzhen Shares [10]. 5. Insurance IT, with recommended stocks including Xinzhi Software and Zhongke Software [11]. Application Stages - Financial institutions' AI applications are categorized into three stages: 1. Initial exploration of large model applications. 2. Development of certain model application capabilities with data accumulation. 3. Achieving deep application of large models [14]. Application Value - AI applications provide value through: 1. Internal cost reduction and efficiency improvement, optimizing operational management and core business processes [21]. 2. External value extraction, enhancing marketing and customer service to improve sales conversion and customer value [21]. Application Pathways - Different types of financial institutions exhibit varied pathways for AI application deployment: 1. Large institutions leverage strong self-research capabilities for deep AI application penetration. 2. Smaller institutions focus on cost-effective solutions, utilizing lightweight models and integrated systems for agile development [26]. AI Empowerment in Banking - AI is enhancing front-office quality and efficiency, optimizing back-office processes across various banking functions [43]. - In credit risk management, AI models can analyze financial data to identify potential risks and improve decision-making processes [47]. AI Empowerment in Securities - The number of securities firms exploring large models is rapidly increasing, with applications extending across various business functions, including investment advisory and research [58][59].

Core Insights - The arrival of the AI financial era signifies a deep exploration from technological refinement to ecological restructuring, enabling financial services to achieve precision, inclusivity, and ecological upgrades [1][2] - The integration of AI into the financial sector is expected to enhance operational efficiency and redefine the core competitiveness of banks, with a target of over 70% penetration of intelligent terminals in the financial sector by 2027 [2][3] Group 1: AI Integration in Banking - Major banks are leading the "AI+" wave, with digitalization significantly improving shareholder returns, averaging 8.2% for leading banks compared to 4.9% for laggards [2] - The Industrial and Commercial Bank of China is implementing a comprehensive AI technology system, focusing on the integration of large and small models to enhance various business applications [3] - China Construction Bank has increased its AI-enabled scenarios from 193 to 274, significantly improving operational efficiency and customer service capabilities [4] Group 2: AI Applications and Innovations - CITIC Bank is exploring AI in customer marketing, management decision-making, and risk control, with over 1,600 intelligent service scenarios established [5][6] - WeBank has achieved a product availability rate exceeding 99.999% and has served over 4.2 billion personal customers, showcasing its advanced digital technology capabilities [8][9] - Ant Group has introduced a new "pay for performance" business model, allowing clients to pay based on the actual results of AI applications, marking a shift from traditional payment models [10] Group 3: International Case Studies and Trends - Morgan Stanley's significant investment in Alibaba highlights the growing trend of capital markets aligning with technology advancements, with a focus on long-term strategies [11][12] - Morgan Stanley's robust financial performance, with a net profit of $58.47 billion in 2024, demonstrates the effectiveness of its dual-engine model combining investment banking and asset management [12][13] - The bank's annual technology investment reached $14 billion in 2023, focusing on cutting-edge technologies such as API interfaces and machine learning [14][15]

Group 1 - The core viewpoint of the news highlights the positive performance of the AI-focused ETF, with a 1.45% increase and a "V" shaped market trend, indicating strong upward momentum [1] - The ETF's constituent stocks showed robust performance, with 26 out of 30 stocks rising, led by Hehe Information with a 6.40% increase, and several others exceeding 4% [1] - The trading volume was significant, exceeding 54 million yuan with a turnover rate of 18.4%, indicating increased liquidity and potential for further capital allocation [1] Group 2 - The 2025 Yunqi Conference reinforced the narrative of growing demand in China's AI and cloud sectors, with continuous improvements in model capabilities, infrastructure, and application ecosystems [2] - The outlook for Alibaba Cloud remains positive, with expectations of accelerating revenue growth on a quarterly basis [2] - The AI-focused ETF closely tracks the STAR Market AI Index, covering high-quality enterprises across the entire industry chain, benefiting from high R&D investment and policy support [2]

Core Insights - The banking industry is rapidly adopting AI applications, with over 100 new scenarios announced by major banks like ICBC, CCB, and BOC as of June 2025, indicating a significant shift towards AI integration in financial services [1][5][6] - A report from Tencent Financial Research Institute highlights that by mid-2025, 79 AI-related projects were awarded in the financial sector, with banks accounting for over half of these projects [1][2] - The Chinese government aims for over 70% application penetration of new intelligent terminals and agents by 2027, increasing to over 90% by 2030, emphasizing the importance of AI in various sectors, including finance [1] AI Application Expansion - Major banks have reported substantial increases in AI application scenarios, with CCB announcing 274 scenarios, up from 193 in 2024, and CITIC Bank claiming over 1,600 scenarios [5][6] - The trend shows that more small and medium-sized banks are beginning to disclose their AI application details, indicating a broader industry engagement with AI technologies [6][8] Efficiency Improvements - AI applications have led to significant efficiency gains, with banks like China Merchants Bank reporting a reduction of 4.75 million hours in labor through AI, translating to approximately 390 million yuan in economic benefits [8] - Traffic Bank reported a 67% increase in output rates and an 83% increase in withdrawal rates through AI deployment in personal banking [8] Challenges in AI Implementation - Despite the rapid adoption, many financial institutions are still in the early stages of AI implementation, facing challenges in integrating AI into core business functions [3][10] - The effectiveness of AI applications varies significantly based on the chosen business scenarios, with some applications proving more successful than others [14][15] Organizational Changes - The integration of AI is prompting a restructuring of banking operations, with a shift from traditional roles to new positions focused on AI and data science [18][20] - Banks are increasingly emphasizing the need for collaboration between technology and business departments to effectively implement AI solutions [20][21] Regulatory Considerations - The financial sector is highly regulated, and the application of AI technologies raises concerns regarding compliance and risk management, necessitating careful oversight [22][23] - Financial institutions are advised to ensure that AI applications align with regulatory requirements and to maintain human oversight in critical decision-making processes [22][25]

Core Insights - The integration of "digital intelligence" and "finance" is creating innovative practices that enhance financial services and support high-quality development [1] - Financial services are increasingly utilizing AI technology, transforming from passive to proactive engagement with customers [1] - Cross-border payment solutions are being enhanced through technological innovations, addressing pain points for foreign visitors in China [2] Group 1: Financial Services Innovation - The financial service exhibition at the China International Fair for Trade in Services showcased the deep integration of AI in finance, with practical applications like intelligent robots providing customer service [1] - Banks are implementing personalized financial solutions through interactive technologies, such as retirement calculators and AR glasses for elderly clients [1] - Financial institutions are focusing on enhancing service breadth and depth by directing resources towards technological innovation [3] Group 2: Support for Technological Innovation - Financial institutions are actively supporting tech innovation by providing comprehensive services across various financial products, including equity investment plans and specialized insurance [3] - The China Banking sector is promoting significant financial support for AI and technology projects, with initiatives like a 1 trillion yuan AI support plan [3] - The financial sector is increasingly utilizing data analytics to provide precise support for small and medium enterprises, enhancing the efficiency of green finance [4] Group 3: Financial Growth Metrics - As of June, loans related to the "five major articles" in finance accounted for approximately 70% of the total loan increment, with growth rates surpassing overall loan growth [5] - The ongoing transformation in finance is reshaping its boundaries while simultaneously catalyzing technological breakthroughs [5]

Core Insights - The company has been awarded the "Best Private Bank in China" for the ninth time by The Asset, highlighting its excellence in comprehensive services within the private banking sector [1][9] Group 1: Business Philosophy and Strategy - The company adheres to the business philosophy of "Integrity and Stability," focusing on national needs, financial capabilities, client expectations, and its own strengths [4] - It has integrated group resources to form a service team of nearly 10,000 people, leading the development of private banking in China [4] Group 2: Wealth Management Services - The company emphasizes a client-centric approach, integrating various investment tools and services to meet diverse client needs [5] - It has generated over 1.2 million professional configuration reports to assist clients in liquidity management, capital preservation, and asset allocation [5] - The company is building an open product ecosystem that matches the diverse needs of clients [5] Group 3: Family and Business Services - The company has launched the "ICBC Chuan Cheng Family" service system, focusing on comprehensive family wealth management, including governance and charitable services [6] - It has established partnerships with over 100 organizations in areas such as trust, insurance, and education to enhance its family services [6] Group 4: Entrepreneurial Support - The company aims to be the "Entrepreneur Partner Bank," providing a comprehensive service ecosystem for entrepreneurs [7] - It has organized nearly 4,000 regional enterprise visits, benefiting over 60,000 entrepreneurs by creating platforms for exchange and collaboration [7] Group 5: Philanthropy and Social Responsibility - Under the "ICBC Bright Action" public welfare brand, the company has engaged in various charitable projects, benefiting nearly 50,000 students in Sichuan [8] - It is exploring new paradigms of "Finance + Charity" to help entrepreneurs fulfill their social responsibilities [8] Group 6: Recognition and Authority - The Asset magazine's 3A award is a prestigious recognition in the Asia-Pacific region for outstanding contributions in wealth management and private banking [9] - The repeated recognition of the company as "Best Private Bank in China" signifies its long-standing professional acknowledgment in the industry [9]

Core Viewpoint - The banking industry is actively pursuing AI development, with various banks announcing projects related to AI capabilities, indicating a significant trend towards AI integration in financial services [1][4][6]. Group 1: AI Deployment Strategies - Different types of banks are forming differentiated AI development paths based on regional characteristics, customer structures, and digitalization foundations [2][5]. - State-owned banks tend to be conservative in their application of financial vertical models, focusing on foundational applications, while city commercial banks and joint-stock banks show a stronger willingness for transformative AI strategies [5]. - Current implementations show that state-owned banks are building platforms and ecosystems, while joint-stock banks emphasize scalability and systematic construction [5]. Group 2: Commonalities Across Banks - All types of banks are focused on how AI can enhance customer experience, optimize business processes, reduce operational costs, and strengthen risk control [6]. - As of August, 31% of customer service centers and remote banking have completed large model deployments within banks [6]. - The total financial technology investment by the six major state-owned banks reached 125.46 billion yuan, a year-on-year increase of 2.15% [6]. Group 3: Challenges in AI Application - The application of AI in financial institutions is primarily focused on general areas, with lower penetration in critical business areas such as marketing and risk control [7][8]. - Three core challenges hinder deeper AI application: technology maturity, professional requirements, and cost considerations [8]. - Financial institutions are currently in a phase of observing and experimenting with AI, particularly in general scenarios, while being cautious in core business areas [8]. Group 4: Technology and Market Dynamics - The integration of finance and AI is driving a dual upward spiral of "technology" and "market" [10]. - Financial institutions are feeling anxious about how to effectively utilize advanced technologies like large models, especially as peers achieve breakthroughs [10]. - The current stage is primarily driven by technology, but as banks recognize AI's value, business demands will increasingly shape technology development [10][11].

Group 1: Transition from Digital Banking to AI Banking - The banking industry is transitioning from digital banking to AI banking, with 2024 being recognized as the "Year of Large Model Applications" [1][2] - AI technologies with deep reasoning and cross-modal capabilities are reshaping the operational environment of banks [2] - The foundational AI strategy for banks includes generative large models and reasoning models, catering to diverse application needs [3][4] Group 2: AI Applications in Banking - Banks are implementing AI applications across various scenarios, including intelligent coding, marketing, customer service, risk control, compliance, and daily management processes [5] - Notable examples include CITIC Bank's integration of AI decision-making and generative models, and China Merchants Bank's AI assistant achieving a 95% accuracy rate in customer intent recognition [5][8] - The number of AI application scenarios disclosed by banks has surged, with major banks like ICBC and CCB enabling numerous applications across various business areas [11] Group 3: Human-AI Collaboration - The relationship between humans and AI is increasingly emphasized, focusing on how employees can effectively utilize AI technologies [9] - Banks are investing significantly in financial technology, with a total investment of 125.46 billion yuan in 2024, reflecting a 2.15% increase from 2023 [11] - The workforce in technology roles is expanding, with notable increases in the number of tech personnel across major banks [12] Group 4: Opportunities and Challenges - AI's widespread application is a key driver of digital transformation in banking, enhancing operational efficiency and customer experience [16] - The banking sector faces challenges related to algorithm compliance, data privacy, and the need for robust AI governance [19][22] - The accuracy of leading financial models is around 95%, indicating ongoing challenges in AI reliability and the need for continuous improvement [22] Group 5: Future Outlook - The integration of AI in banking is expected to lead to comprehensive automation and intelligent services, fundamentally changing operational models [17][23] - The year 2025 is anticipated to be a pivotal period for rapid AI application growth in the financial services sector [23]

Core Insights - The rapid development of financial technology is driving banks to adopt large model technology as a core driver for transformation and innovation, with many banks actively engaging in this area by 2025 [2] Group 1: Industrial Leadership - Industrial and Commercial Bank of China (ICBC) leads in large model application, having launched the "ICBC Zhiyong" system, which has surpassed 1 billion calls by Q2 2025, enhancing over 20 core business areas and 200 application scenarios [3] - ICBC's application scenarios increased by 67% year-on-year compared to 2024, with call frequency rising by 120%, showcasing significant scaling effects [3] - The system has improved foreign exchange trading decision response speed by 80% and increased trading execution efficiency by 300%, with related business revenue up 15% year-on-year in the first half of 2025 [3] Group 2: Technological Deployment - Agricultural Bank of China has successfully deployed the DeepSeek model internally, enhancing business innovation and operational efficiency across various processes [5] - Huaxia Bank has implemented DeepSeek for various applications, improving office efficiency and customer service through intelligent Q&A and report generation tools [6] - Jiangsu Bank utilizes DeepSeek for intelligent contract quality inspection and automated valuation reconciliation, achieving over 90% success in identification [7] Group 3: Customer Service Enhancements - Customer service improvements include a 30% increase in marketing conversion rates through targeted marketing strategies based on customer data analysis [7] - Customer satisfaction has risen from 80% to over 90% due to enhanced intelligent customer service capabilities [7] - Beijing Bank has developed a proprietary "Jingzhi" large model, focusing on building an AI platform for various applications [8] Group 4: Future Directions - Shanghai Bank is constructing a "large model + micro model" collaborative system to enhance service delivery and operational efficiency across various financial services [9] - Chongqing Bank plans to leverage large models for broader applications in marketing, risk control, and internal management by 2025 [8] - The overall trend indicates that banks are not only improving their operational efficiency and service quality but also contributing valuable experiences for the digital transformation of the banking industry [10]